Speaker
Description
The use of hadrons - such as protons, helium, and carbon ions—in radiotherapy requires highly precise Relative Stopping Power (RSP) maps of patient anatomy to minimize range uncertainties. Using the aforementioned hadrons for imaging before the treatment offers higher reconstruction quality and dosimetric advantage in comparison to conventional X-ray CT for this purpose. However, executing the simulations to generate data for the image reconstruction, the reconstruction of the RSP maps, investigating the intrinsic imaging capabilities and dose convergence of different particle species require immense computational resources.
To overcome these computational bottlenecks, the entire data generation and processing pipeline of this study was executed utilizing the advanced high-performance GPU infrastructure of the Wigner Scientific Computing Laboratory (WSCLAB). We performed comprehensive GATE/Geant4 Monte Carlo simulations of a CTP404 phantom across $360^\circ$ projections for protons, helium, and carbon ions. The massively parallel architecture of the WSCLAB GPUs enabled the efficient generation of baseline high-statistics datasets and facilitated the iterative algorithm necessary to reconstruct the RSP maps.
By leveraging this computing power, we successfully mapped the convergence thresholds required for stable RSP recovery. Our findings indicate that carbon-ion and helium-ion imaging can achieve RSP accuracy competitive with Dual-Energy CT, maintaining relative errors well below 1% for the vast majority of the materials. Furthermore, dosimetric scaling demonstrated that these high-fidelity maps can be obtained at competitive or significantly reduced absorbed doses compared to standard modalities.